Interactively exploring supply and demand in the UK independent music scene

We present an exploratory data mining tool useful for finding patterns in the geographic distribution of independent UK-based music artists. Our system is interactive, highly intuitive, and entirely browser-based, meaning it can be used without any additional software installations from any device. The target audiences are artists, other music professionals, and the general public. Potential uses of our software include highlighting discrepancies in supply and demand of specific music genres in different parts of the country, and identifying at a glance which areas have the highest densities of independent music artists

Detecting trends in twitter time series

Detecting underlying trends in time series is important in many settings, such as market analysis (stocks, social media coverage) and system monitoring (production facilities, networks). Although many properties of the trends are common across different domains, others are domain-specific. In particular, modelling human activities such as their behaviour on social media, often leads to sharply defined events separated by periods without events. This paper is motivated by time series representing the number of tweets per day addressed to a specific Twitter user. Such time series are characterized by the combination of (1) an underlying trend, (2) concentrated bursts of activity that can be arbitrarily large, often attributable to an event, e.g., a tweet that goes viral or a real-world event, and (3) random fluctuations/noise. We present a new probabilistic model that accurately models such time series in terms of peaks on top of a piece-wise exponential trend. Fitting this model can be done by solving an efficient convex optimization problem. As an empirical validation of the approach, we illustrate how this model performs on a set of Twitter time series, each one addressing a particular music artist, which we manually annotated with events as a reference

SuMoTED : an intuitive edit distance between rooted unordered uniquely-labelled trees

Defining and computing distances between tree structures is a classical area of study in theoretical computer science, with practical applications in the areas of computational biology, information retrieval, text analysis, and many others. In this paper, we focus on rooted, unordered, uniquely-labelled trees such as taxonomies and other hierarchies. For trees as these, we introduce the intuitive concept of a ‘local move’ operation as an atomic edit of a tree. We then introduce SuMoTED, a new edit distance measure between such trees, defined as the minimal number of local moves required to convert one tree into another. We show how SuMoTED can be computed using a scalable algorithm with quadratic time complexity. Finally, we demonstrate its use on a collection of music genre taxonomies